Life science researchers routinely obtain images of mixtures of macromolecules, such as DNA, RNA and proteins, and their fragments, from stained gel electrophoresis samples and Western blots. The images are then captured and analyzed to obtain data.
In order to separate the complex mixtures using electrophoresis, several samples containing the mixture are applied to separate, spaced apart locations on the electrophoresis gel. An electrical current is then applied to the gel, which causes the individual samples to migrate through the gel within their prescribed lane or track, thereby generating an invisible lane on the gel. The complex mixture is then separated by size, i.e., molecular weight, and net charge in the gel matrix. The larger, higher molecular weight with low net charge molecules remain relatively nearer the place of sample loading on the gel or membrane. The smaller, lower molecular weight molecules with high net charge migrate farther from the sample loading place of the gel or membrane. Each individual segregation of sample is then identified as a band. The gel can then be stained for total sample visualization, or transferred to a membrane for visualization of a specific target of interest by blotting (Western blotting in the case of proteins, Southern blotting in the case of DNA, and Northern blotting in the case of RNA). The researcher then images the gel, membrane or blot, collectively termed a substrate or object, to analyze the target(s) of interest for amount, relative or absolute, purity, and molecular weight. Such analysis requires detection and identification of the lanes and bands in the image.
The images of the object are typically acquired using one or more visualization means, such as ultra violet illumination, white light illumination, fluorescence, or chemiluminescence.
Finding proper exposure time is an important factor affecting image quality and it is important for successful, accurate pixel intensity measurement on the acquired image. Various auto exposure methods have been developed, but those methods are either complex, inaccurate and/or disregard user input. Optimal exposure time for image capture is not always dependent on pixel intensity of the entire image or any particular region of the image. In some cases, the user/operator can best define which object(s) on the captured image should be the target for optimal exposure determination.
Images of the substrate, or of other objects, can be captured by any of a wide variety of structures or devices, but in one case takes the form of an imaging device or image capture device utilizing/comprising a CCD (Charge Coupled Device), but can also be used with autoradiography, scanners, CMOS (Complementary Metal Oxide Semiconductor) imagers, phosphor imagers, and others. In the case of the CCD, such a system utilizes an array of light-sensitive optical elements, such as pixels or other light sensing units. The pixels are configured such that when light (photons) are detected by the pixels, each pixel provides an output in the form of an electrical signal that is proportional or related to the intensity of the detected light (photons). Multiple pixels or arrays of pixels can also be combined together using the well-known binning technique, and in this case each group of binned pixels can be considered a single pixel.
Each pixel has a limited capacity for maximum light exposure, also known as its saturation point. If too many pixels reach their saturation point for a given image, the image is considered over-exposed. In contrast, if too many of the pixels receive insufficient light, the image lacks sufficient contrast and is considered under-exposed. Thus, when capturing images it is helpful to determine the optimal exposure time so that data from the image can be accurately captured. Use of the optimal exposure time maximizes the dynamic range of the pixel intensities in the image, and minimizes the number of pixels that are saturated.
In previous systems, in order to determine the proper exposure time for image acquisition, a trial-and-error image acquisition process or a complex, inaccurate automatic exposure method was utilized. The user/operator would need to carry out multiple image acquisitions with differing exposure times, compare the images, and make estimates as to the best exposure time. However this process is labor-intensive and also takes up usage of the imaging equipment that would otherwise be put to productive use.